Abstract
In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analysed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model – the ability to form clusters – is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified.
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Sharpanskykh, A., Treur, J. (2013). Modelling and Analysis of Social Contagion Processes with Dynamic Networks. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_5
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DOI: https://doi.org/10.1007/978-3-642-40495-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40494-8
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